TUMO Creative Technologies

Personal Geographies: Exploring Your Community Through the Lens of Data Visualization

Day 2 (January 13th, 2021)

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https://tumopersonalgeographies.github.io/

Iterinary for Today’s Class:

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Database Presentations

Visualization Objectives

Encoding Visualization Information

Legends and Data Labels

Gestalt Psychology

Design Critiques

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Why do we visualize data?

After finding a dataset and analyzing its source, credibility, and structural organization, the visualizations you choose will depend on what research questions you’re setting out to investigate. Ask yourself whether you want to…

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• Explore the correlation between variables
• Ascertain what causal relationships exist in a database
• Better understand or disseminate complex trends from large volumes of data
• Evaluate the extent, direction, and nature of change over time
• Tell a story with statistical backing

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How is data encoded visually?

In order to describe the means of representing different data types, it is helpful to lay the groundworks for some vocabulary…

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• A mark refers to a fundamental graphical unit such as a point, line, or area
• A channel, on the other hand, defines the aesthetic qualities of a mark and can take the form of position, color, shape, angle, or even length

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How is data encoded visually? (cont.)

Source: Munzner, Tamara, and Eamonn Maguire. Visualization Analysis & Design. , 2015. Internet resource. (Chapter 5 Figure 5.2)

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How is data encoded visually? (cont.)

Source: Munzner, Tamara, and Eamonn Maguire. Visualization Analysis & Design. , 2015. Internet resource. (Chapter 5 Figure 5.3)

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Activity 2.1: Identifying marks and channels

(10 minutes)

For the following set of visualizations, try to identify examples of different marks and channels. Think critically about why the author of the visualization chose to represent the data in that way. Also speculate whether you think a certain mark or channel improves or hinders the clarity of the overall visualization. You may even offer suggestions for other channels or marks that could be used to better convey the message of the visualization…

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Activity 2.1: Identifying marks and channels

(10 minutes)

Source: Jeffrey Shaman, New York Times

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Visualization Title: “Here’s When We Expect Omicron to Peak”

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Activity 2.1: Identifying marks and channels

(10 minutes)

Source: Nathan Yau, Flowing Data

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Visualization Title: “A Day in the Life of Women and Men”

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Activity 2.1: Identifying marks and channels

(10 minutes)

Source: Christian Davenport, John Muyskens, Youjin Shin, Monica Ulmanu, Washington Post

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Visualization Title: “FAA-licensed launch sites Sites: where companies have active licenses to conduct launch activities”

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How is the type of visualization related to

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its objective?

Every research questions (and by proxy, different visualizations) are more or less suited to certain types of visualizations over others. Here are some general heuristics to help you in narrowing down which type to choose…

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• You’re interested in the distribution of values… histograms
• You’re working with categorical data… bar chart, pie chart
• You’re curious about the relationship between variables… line chart, scatter plot
• You want to compare the relative size of different areas… bubble chart, area chart

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How is the type of visualization related to

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its objective?

Source: Jorge Camoes, excelcharts.com

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Article Title: “Wordless instructions for making charts: Tableau Edition”

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How do we label data points?

The need to identify specific values that make up the data points in a visualization will vary according to its objective. Sometimes, it’s sufficient to focus on larger trends at a high level of abstraction, while in other cases, that information is crucial. Here are some options in either case…

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• Tooltips that respond in real-time to a user’s mouse movements
• Fixed data labels
• Continuous or discrete scales

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Data legends

How would you define a data legend?

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Data legends

As the complexity of a visualization grows and (so does the amount of variables represented), there can be an increasing need to use external labels to identify the possible range of values a variable can assume.

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Data legends set out to fill this need, clarifying what a given channel represents. They are typically positioned to the side of a visualization, and it’s recommended to order them sequentially if the data is quantitative.

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Discrete vs. continuous color scales

One specific type of legend used to map out color values is called a color scale. They can be used to develop a visual connection between hex-codes and a corresponding range of data values. There are two primary types of color scales…

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• Discrete, suited for categorical/ordinal data or quantitative data in which you want to draw attention to specific values or explicitly point out patterns
• Continuous, suited for quantitative data in which you want to present a wider spectrum of possible color/data values

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Discrete vs. continuous color scales

Source: Chris Henrick, Observable

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Article Title: “Color Scales: Continuous vs Discrete”

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What are the psychological underpinnings

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of different design decisions?

Gestalt psychology, a 20th century design philosophy, seeks to understand how we recognize patterns and process information visually. Applying this framework to the context of user interface and user experience design can help us understand how our visualizations are perceived by readers. There are 5 core principles in gestaltism…

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• Proximity
• Similarity
• Continuation
• Closure
• Figure/Ground

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Break ends at 6:00pm

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Activity 2.2: Reading about Gestalt psychology

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(20 minutes)

Using the link provided in this lecture is Google Doc, please read the 2 articles about Gestaltism…

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• Please take notes and pay attention to the connection between each core principle and the visualizations we’ve taken a look at in class

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Activity 2.3: Applying Gestalt psychology

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(15 minutes)

In a similar fashion to Activity 1.5, you will examine a series of visualizations and try to pinpoint which principles in Gestalt psychology appear in any given one. Please frame your thoughts in the context of whether or not the visualization successfully adheres to the design principle in question…

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Activity 2.3: Applying Gestalt psychology

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(15 minutes)

Source: Kevin Hamm, Denver Post

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Visualization Title: “Marshall fire: Map of destroyed or damaged homes and businesses”

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Activity 2.3: Applying Gestalt psychology

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(15 minutes)

Source: Jeff Desjardins, New York Times

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Article Title: “The Times They Are A-Changin”

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Activity 2.3: Applying Gestalt psychology

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(15 minutes)

Source: Dr. Angelique Coetzee, Daily Mail Australia

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Article Title: “Another 75 cases of Omicron Covid are found in England: UK total doubles as health chiefs reveal the majority of sequenced super-variant infections are in fully vaccinated people”

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Activity 2.4: Comprehensive design

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critiques (1 hour)

For the final activity of today’s class, please search the web for 5 visualizations which interest you and write a detailed design critique of each, incorporating vocabulary learned in today’s lecture and at least 3 of Gestalt’s principles per visualization.

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Comment on what you do or don’t like about the style of data representation. Offer avenues the author could pursue to improve their visualization. Note any significant findings, trends, or takeaways you encounter. Prepare to present your chosen visualizations to the class…

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Please share your document

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with the email address

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ethanmcfarlin@gmail.com

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Thank You

for your attention